Deep Convolutional Neural Networks (Deep CNNs) are the heart of the remarkable development in the field of deep learning. CNNs have already been used in the 90s to solve the problem of character recognition, but the reason of becoming as famous as it is now is thanks to recent research. The deep CNNs won the ILSVRC-2012 competition. Then, the convolutional neural network became a very useful tool in the field of machine learning.
Image segmentation, on the other hand, takes a training image or a test image as an input and produces a label image as an output. The deep learning has recently become popular. For example, the deep learning is used for the image segmentation.
Meanwhile, various methods for improving a performance of such segmentation are currently presented.
As one of such methods, when performing the segmentation, a user may desire to enhance the accuracy of the segmentation by using several CNNs. Namely, after the same input data is inputted to a plurality of CNN devices, respective outputs of the CNN devices are combined to generate a combined output, but in this case, there may be problems that initial values of parameters of the plurality of CNN devices should be randomly set every time, and in order to obtain a result of the image segmentation, the plurality of CNN devices should be individually learned.